CN111639526A - Power transmission line target detection and monitoring method based on deep learning - Google Patents
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Abstract
The invention discloses a power transmission line target detection and monitoring method based on deep learning, which is characterized in that after a large amount of image data are collected in a power transmission line target detection scene, the image data are cleaned and screened according to a certain positive and negative sample proportion, and then dangerous targets are accurately labeled manually and are divided into a training set, a verification set and a testing set; performing data amplification methods such as zooming, rotating, cutting, turning and the like before model training; constructing a model of the improved Huge-YOLO v3 on a Darknet deep learning framework, training by means of the latest powerful software and hardware computing equipment, storing the model according to loss reduction change in the training process, judging through some evaluation indexes, and finally, summarizing the index evaluation score and selecting the model with the best convergence fitting effect; the method can acquire higher-level semantic information and lower-level fine-grained characteristic information, thereby improving the target detection accuracy and solving the problem of huge difference of target scales.
Description
Technical Field
The invention relates to the technical field of power system monitoring, in particular to a power transmission line target detection and monitoring method based on deep learning.
Background
A large amount of image data are collected in a monitoring system for preventing external damage of a power transmission line, and a target with potential threat to the line can be detected and early warned through a deep learning target detection technology, for example, targets such as a person who is lifted by a steam, a fishing person and the like can cause interference damage to a wire. The deep learning model has strong representation capability and can play an important role in the technical field of image target monitoring. The object detection focuses on the category information and the position information of a specific object, and currently, a mainstream framework is mainly divided into two detection models, namely a two-stage (two-stage) detection model and a single-stage (one-stage) detection model.
YOLO v3 is based on a better basic classification network (class ResNet) and a classifier (Darknet-53), and simultaneously absorbs the multi-scale prediction idea of a pyramid feature representation method (FPN), YOLO v3 performs size transformation in the image tensor forward process, increases step size processing through a convolution kernel, scales for 3 times to reach 1/32, extracts three layers of feature maps (the sizes are respectively 52 × 52, 26 × 26 and 13 × 13) from different scales, performs independent prediction on each layer, performs splicing and fusion for further prediction in an up-sampling mode, finally performs prediction by using 9-scale anchor box clustered by dimensions, and performs prediction by evenly distributing the anchor box on 3 layers of feature maps, wherein the receptive fields of the anchor box correspond to 8 × 8, 16 × 16 and 32 × 32 respectively. The conversion of the YoLO v3 class loss function from Softmax to a Sigmoid function solves the same type of large and small classification problems. While YOLO v3 performed well in terms of speed, multi-scale training was not particularly effective in detecting object targets that were extremely small and large in order to strike a tradeoff between speed and accuracy.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a power transmission line target detection and monitoring method based on deep learning, which is improved on the basis of single-stage YOLO v3 and solves the problem that the shot images of a power transmission line scene contain large difference of target scale; the method for extracting and fusing the features at different layers of the YOLO v3 by multi-scale prediction is improved, and higher-layer semantic information and lower-layer fine-grained feature information can be obtained, so that the target detection accuracy is improved, and the problem of huge difference of target scales is solved.
The invention relates to a power transmission line target detection and monitoring method based on deep learning, which comprises the following steps:
(1) acquiring an image of a specific target through monitoring shooting equipment on a power transmission line site;
(2) screening the images in the step (1) according to a positive sample-negative sample ratio of 3: 7-5: 5, selecting images with proper angles, and deleting images without targets or with unclear angles, wherein the positive samples are images containing specific targets, and the negative samples are images without specific targets;
(3) labeling specific targets in the screened images by using a LabelImg tool, and dividing the images into a training set, a verification set and a test set;
(4) amplifying an image sample before model training, and amplifying the image by adopting a scaling, rotating, cutting or turning mode;
(5) constructing a model on a Darknet deep learning framework by using an improved Huge-YOLO v 3;
(6) training by means of a GPU Nvidia GTX 1080Ti hardware environment based on the constructed model; continuously adjusting parameters in the training process, optimizing the loss function loss value to the minimum, storing different models according to loss reduction change, performing comprehensive judgment through accuracy, recall rate, IoU and mAP evaluation indexes, and selecting the model with the best convergence fitting effect as a detection and monitoring model;
(7) inputting the image to be detected into a trained detection and monitoring model, carrying out model reasoning and identification on the type and position of the specific target of the image and the credibility of the target, sending early warning information to related power transmission line inspection workers through an alarm system when the specific target with potential harm appears, and processing the specific target according to the actual condition by the inspection workers.
The improvement of the Huge-Yolo v3 is that the size of an input image is changed from 416 × 416 × 3 to 832 × 832 × 3, a network layer is added on a main structure Darknet-53, and four feature maps of 104 × 104, 52 × 52, 26 × 26 and 13 × 13 are respectively extracted; and fusing the four-layer characteristic diagram by adopting a self-adaptive characteristic fusion mode to obtain fused characteristics, wherein the formula is as follows:
The method has the advantages and the technical effects that:
the method of the invention can well adapt to the problem of large scale change based on the improved Huge-YOLO v3 model of YOLO v3, and the recognition accuracy effect is obviously improved; the semantic information of a higher layer and the fine-grained characteristic information of a lower layer can be obtained, so that the target detection accuracy is improved, and the problem that the target scale is greatly different is solved.
Drawings
FIG. 1 is a flowchart of Huge-YoLO v3 model training;
FIG. 2 is a diagram of a four-level feature map fusion process;
FIG. 3 is a diagram illustrating a specific object detected in an item;
FIG. 4 is a diagram illustrating a specific object detected in an item;
FIG. 5 is a diagram illustrating a specific object detected in a project.
Detailed Description
The present invention is further illustrated by the following examples, but the scope of the invention is not limited to the above-described examples. Example 1: as shown in fig. 1, the method for detecting and monitoring the target of the power transmission line based on deep learning comprises the following steps:
in this embodiment, a GPU Nvidia GTX 1080Ti hardware environment is used to complete a training test; adopting a Darknet compiling environment, wherein a Ubuntu system is required to be installed in a hardware environment, and meanwhile, relevant software (drivers, CUDA and CUDNN) such as NVIDIA and the like are installed for a GPU acceleration training model and an image processing relevant library such as OpenCV and the like;
to increase the size of large objects (e.g., a car crane), a larger receptive field and higher-level semantic information are needed, while to far away small objects (e.g., a person), fine-grained information in the underlying features is needed to be resolved. In the training process of the deep learning target detection model, the number of samples of data and the refinement degree of labels are one of three key factors for determining the fitting of the model. Therefore, after a large amount of image data are collected according to a power transmission line target detection scene, the image data are cleaned and screened according to a certain positive and negative sample proportion, and then dangerous targets are accurately marked manually and are divided into a training set, a verification set and a test set. The model training is preceded by data augmentation methods such as scaling, rotation, cropping, flipping, etc. Constructing a model of the improved Huge-YoLO v3 on a Darknet deep learning framework, training the model by means of the latest strong software and hardware computing equipment, storing the model according to loss reduction change in the training process, and judging according to some evaluation indexes, such as: accuracy (Precision), Recall (Recall), iou (interaction over union), mep (mean Average Precision), and the like. Finally, selecting a model with the best convergence fitting effect in conclusion of the index evaluation score;
1. data acquisition, namely uploading and collecting a large amount of image data through monitoring shooting equipment on the site of the power transmission line;
2. cleaning and screening data, namely cleaning and screening according to a positive sample and negative sample ratio of 5:5, specifically operating and selecting images with proper angles, deleting a large number of images without targets or with unclear and high repeatability, and ensuring data diversity, wherein the positive sample is an image containing a specific target;
3. data labeling, namely manually and accurately labeling a specific target in an image by using a LabelImg tool to generate a label file, wherein the data is in a VOC (volatile organic compound) format and is divided into a training set, a verification set and a test set;
4. and (3) data amplification, namely amplifying the data sample before model training, such as: the method comprises the following steps of scaling, rotating, cutting, turning and the like, and aims to increase the diversity of training data, improve the accuracy and generalization capability of a model and avoid overfitting;
5. model training, downloading pretraining weight based on ImageNet, constructing a model on a Darknet deep learning framework by using improved Huge-YoLO v3, and modifying a training configuration file, wherein parameters such as batch-64 and learning-rate-0.001 are used;
in order to adapt to improvement of mobile and embedded edge equipment based on YOLO v3, the Tiny-YOLO v3 greatly simplifies a network structure, changes a characteristic diagram from three layers into two layers, and ensures the speed in an environment with limited resources at the cost of reducing accuracy; and the speed requirement of the power transmission line target detection scene is not real-time detection, so that the accuracy can be greatly improved by depending on strong rear-end computational resources, and Huge-YOLO v3 is provided based on YOLO v 3.
Improved relation table of corresponding relation among feature diagram, receptive field and anchor box size
The method mainly comprises the steps of changing the size of an input image from 416 x 3 to 832 x 3, adding a network layer on a backbone structure Darknet-53, respectively extracting four Feature maps of 104 x 104, 52 x 52, 26 x 26 and 13 x 13, and adopting an adaptive Feature Fusion mode (ASFF) to Adaptively adjust and learn a Fusion weight through scale map resize to respectively fuse different Feature layers to a final Feature map as the size relations of the Feature maps, the receptive field and the anchor box in the table correspond to each other so as to more fully acquire high-level semantic information and bottom-level fine-grained information and not to follow the original up-sampling splicing mode;
as shown in FIG. 2, the dotted box represents the fusion process for the four-layer feature map, L1，L2，L3，L4Respectively corresponding to the characteristics of four layers of Level1, Level2, Level3 and Level4, and then respectively multiplying the characteristics by a weight parameter a4，β4，γ4，λ4And summing to obtain the fused features, wherein the formula represents:
adding the weight parameters to ensure that the characteristics of all layers are the same and the channel number is the same, adjusting the up-sampling or down-sampling channel number of different characteristic layers, obtaining the weight parameters through convolution of a characteristic diagram after resize by 1 × 1, and obtaining the parameter a4，β4，γ4，λ4After concat, limiting the value range to [0, 1 ] by a SoftMax function]And the sum is 1;
6. model evaluation, namely continuously adjusting parameters in a network, optimizing the loss function loss value to the minimum, storing different models according to loss reduction change, and judging through evaluation indexes such as accuracy (Precision), Recall rate (Recall), IoU (interaction Unit), mAP (mean Average precson) and the like;
7. comprehensively judging by the evaluation mode, and selecting a model with the best convergence fitting effect;
8. inputting an image to be detected into a trained model, carrying out model reasoning and identification on the type and position of a specific target of the image and the credibility of the target, judging according to preset alarm setting in the system, and sending early warning information to related power transmission line inspection workers through an alarm system when the specific target with potential harm, such as 'steam hanging', appears, wherein the equipment commonly used in the place, such as a mobile phone APP, is properly processed by the inspection workers according to actual conditions;
9. fig. 3 shows a specific target detected in a certain item, a detection box frame of the target at the lower left corner near the shooting device is large, and a box frame of the targets far away from the shooting device in fig. 4 and 5 is small; the improved Huge-YOLO v3 model based on YOlO v3 can well adapt to the problem of large scale change, and the recognition accuracy effect is obvious.
Claims (2)
1. A power transmission line target detection and monitoring method based on deep learning is characterized by comprising the following steps:
(1) acquiring an image of a specific target through monitoring shooting equipment on a power transmission line site;
(2) screening the images in the step (1) according to a positive sample-negative sample ratio of 3: 7-5: 5, selecting images with proper angles, and deleting images without targets or with unclear angles, wherein the positive samples are images containing specific targets, and the negative samples are images without specific targets;
(3) labeling specific targets in the screened images by using a LabelImg tool, and dividing the images into a training set, a verification set and a test set;
(4) amplifying an image sample before model training, and amplifying the image by adopting a scaling, rotating, cutting or turning mode;
(5) constructing a model on a Darknet deep learning framework by using an improved Huge-YOLO v 3;
(6) training by means of a GPU Nvidia GTX 1080Ti hardware environment based on the constructed model; continuously adjusting parameters in the training process, optimizing the loss function loss value to the minimum, storing different models according to loss reduction change, performing comprehensive judgment through accuracy, recall rate, IoU and mAP evaluation indexes, and selecting the model with the best convergence fitting effect as a detection and monitoring model;
(7) inputting the image to be detected into a trained detection and monitoring model, carrying out model reasoning and identification on the type and position of the specific target of the image and the credibility of the target, sending early warning information to related power transmission line inspection workers through an alarm system when the specific target with potential harm appears, and processing the specific target according to the actual condition by the inspection workers.
2. The deep learning-based power transmission line target detection and monitoring method according to claim 1, characterized in that: the improvement of Huge-Yolo v3 is that the size of an input image is changed from 416 × 416 × 3 to 832 × 832 × 3, a network layer is added on a main structure Darknet-53, and four feature maps of 104 × 104, 52 × 52, 26 × 26 and 13 × 13 are respectively extracted; and fusing the four-layer characteristic diagram by adopting a self-adaptive characteristic fusion mode to obtain fused characteristics, wherein the formula is as follows:
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